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Title: Uncertainty considerations in calibration and validation of hydrologic and water quality models

Author
item Guzman Jaimes, Jorge
item SHIRMOHAMMADI, ADEL - University Of Maryland
item Sadeghi, Ali
item WANG, XIUYING - Texas A&M Agrilife
item CHU, MA - St Louis University
item JHA, MANOJ - North Carolina Agricultural And Technical State University
item PARAJULI, PREM - Mississippi State University
item Harmel, Daren
item KHARE, YOGESH - University Of Florida
item HERNANDEZ, JAIRO - Boise State University

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2014
Publication Date: 12/1/2015
Citation: Guzman Jaimes, J.A., Shirmohammadi, A., Sadeghi, A.M., Wang, X., Chu, M.L., Jha, M.K., Parajuli, P., Harmel, R.D., Khare, Y., Hernandez, J. 2015. Uncertainty considerations in calibration and validation of hydrologic and water quality models. Transactions of the ASABE. 58(6):1745-1762.

Interpretive Summary: Hydrologic and water quality models (HWQMs) are increasingly used to support decisions on the state of various environmental issues and policy directions on present and future scenarios. Uncertainty associated with such models may impact the capacity of scientist to properly evaluate the response of complex systems leading to misguided assessments and risk management decisions. HWQMs require input data, many of which are not known with certainty and in other cases model users are not aware of sources of uncertainty. Various uncertainty assessment methods have been used with different HWQMs creating concerns to what is the more adequate approach for handling uncertainty in such models and how it can be implemented across various degrees of discretization complexities and scales. In this paper, our primary intention was to review uncertainty associated to HWQMs and to provide guidance and useful information for researchers and investigators. In this regards, our aim was also to introduce best possible approaches to quantify model uncertainties, especially during HWQM calibration and validations. More specifically, we explored the genesis of uncertainty in hydrologic and water quality modeling (i.e., spatiotemporal scales, model representation, model discretization, model parameterization), and provide strategies for assessing uncertainty in hydrologic and water quality modeling on local and global scales when interpreting the model output.

Technical Abstract: Hydrologic and water quality models (HWQMs) are increasingly used to support decisions on the state of various environmental issues and policy directions on present and future scenarios, at scales varying from watershed to continental levels. Uncertainty associated with such models may impact the capacity of the models to accurately evaluate the response of complex systems leading to misguided assessments and risk management decisions. Current well-known HWQMs contain numerous input parameters, many of which are not known with certainty and in other cases model users can hardly recognize the genesis of uncertainty. Uncertainty in data, model structure, and model parameters can propagate throughout model runs, causing the model output to substantially deviate from the expected response of the natural system. Various uncertainty assessment methods have been used with different HWQMs creating concerns to what is the more adequate approach for handling uncertainty in such models and how it can be implemented across various degrees of discretization complexities and scales. In this paper, our primary intention was to review uncertainty in the currently used HWQMs and to provide guidance and useful information for researchers and investigators. In this regards, our aim was also to introduce best possible approaches to quantify model uncertainties, especially during HWQM calibration and validations. More specifically, we explored the genesis of uncertainty in hydrologic and water quality modeling (i.e., spatiotemporal scales, model representation, model discretization, model parameterization), and provide strategies for assessing uncertainty in hydrologic and water quality modeling on local and global scales when interpreting the model output.